Cooperative Profit Sharing in Coalition Based Resource Allocation in Wireless Networks

Abstract

We consider a network in which several service providers offer wireless access service to their respective subscribed customers through potentially multi-hop routes. If providers cooperate, i.e., pool their resources, such as spectrum and base stations, and agree to serve each others' customers, their aggregate payoffs, and individual shares, can potentially substantially increase through efficient utilization of resources and statistical multiplexing. The potential of such cooperation can however be realized only if each provider intelligently determines who it would cooperate with, when it would cooperate, and how it would share its resources during such cooperation. Also, when the providers share their aggregate revenues, developing a rational basis for such sharing is imperative for the stability of the coalitions. We model such cooperation using transferable payoff coalitional game theory. We first consider the scenario that locations of the base stations and the channels that each provider can use have already been decided apriori. We show that the optimum cooperation strategy, which involves the allocations of the channels and the base stations to mobile customers, can be obtained as solutions of convex optimizations. We next show that the grand coalition is stable in this case, i.e. if all providers cooperate, there is always an operating point that maximizes the providers' aggregate payoff, while offering each such a share that removes any incentive to split from the coalition. Next, we show that when the providers can choose the locations of their base stations and decide which channels to acquire, the above results hold in important special cases. Finally, we examine cooperation when providers do not share their payoffs, but still share their resources so as to enhance individual payoffs. We show that the grand coalition continues to be stable. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of the University of Pennsylvania's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from Abstract-We consider a network in which several service providers offer wireless access service to their respective subscribed customers through potentially multi-hop routes. If providers cooperate, i.e., pool their resources, such as spectrum and base stations, and agree to serve each others' customers, their aggregate payoffs, and individual shares, can potentially substantially increase through efficient utilization of resources and statistical multiplexing. The potential of such cooperation can however be realized only if each provider intelligently determines who it would cooperate with, when it would cooperate, and how it would share its resources during such cooperation. Also, when the providers share their aggregate reve...

Spectrum sharing in multi-channel cooperative cognitive radio networks: a coalitional game approach

“…where U p_m and U s_k are given in (5) and (7), respectively, and the set of time fractions we need to solve in (8) are…”

“…We propose a coalition game theoretic framework [7][8][9] to understand how to exploit SUs' involvement so that optimal fraction of time allocation can be obtained. In addition, flexible and effective cooperation among PUs and SUs can be achieved and conflicting interests between them can be resolved.…”

Cooperative Profit Sharing in Coalition-Based Resource Allocation in Wireless Networks

“…Convexity 1 of a game is a sufficient condition for the nonemptiness of the core. The games we consider, however, are not convex ( [4]). Nevertheless, in the following sections we show that the cores of the coalitional games for different network setups are nonempty, and we also obtain imputations in the respective cores.…”

“…Consider the following convex optimization problem which returns v(S) for any coalition (3) ensure that the total fraction of time each customer is served, is at most 1. A channel can serve at most the whole fraction of time by (4). Constraints (5) guarantee the minimum service rates.…”

“…If now a group of providers agree to cooperate by pooling their base stations and customers, not only can they benefit from sharing other's base stations, but they also enjoy a larger set of relay nodes. This, in turn, can increase the capacity of the network, as well as its power efficiency 4 . Therefore, cooperation in multi-hop networks has even higher potential than that in a single hop network.…”